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Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM

Author

Listed:
  • Fei Qian

    (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China)

  • Li Chen

    (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China)

  • Jun Li

    (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China)

  • Chao Ding

    (State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230029, China)

  • Xianfu Chen

    (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China)

  • Jian Wang

    (State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230029, China)

Abstract

Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.

Suggested Citation

  • Fei Qian & Li Chen & Jun Li & Chao Ding & Xianfu Chen & Jian Wang, 2019. "Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM," IJERPH, MDPI, vol. 16(12), pages 1-14, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:12:p:2133-:d:240360
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    References listed on IDEAS

    as
    1. Rongxiao Wang & Bin Chen & Sihang Qiu & Zhengqiu Zhu & Yiduo Wang & Yiping Wang & Xiaogang Qiu, 2018. "Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases," IJERPH, MDPI, vol. 15(7), pages 1-19, July.
    2. Tianjun Zhang & Shuang Song & Shugang Li & Li Ma & Shaobo Pan & Liyun Han, 2019. "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series," Energies, MDPI, vol. 12(1), pages 1-15, January.
    3. Xin Gao & Xiaobing Li & Bing Zhao & Weijia Ji & Xiao Jing & Yang He, 2019. "Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection," Energies, MDPI, vol. 12(6), pages 1-18, March.
    4. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
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    Cited by:

    1. Yuchen Wang & Zhengshan Luo & Yulei Kong & Jihao Luo, 2024. "Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture," Sustainability, MDPI, vol. 16(11), pages 1-19, May.

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